A swaption volatility model using Markov regime switching

2008 ◽  
Vol 12 (1) ◽  
pp. 79-114 ◽  
Author(s):  
Richard White ◽  
Riccardo Rebonato
Keyword(s):  
Regime Switching ◽  
Markov Regime Switching ◽  
Volatility Model

scholarly journals Volatility Model Choice for Sub-Saharan Frontier Equity Markets - A Markov Regime Switching Bayesian Approach

2019 ◽  
Vol 9 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Carl Hope Korkpoe ◽  
Nathaniel Howard
Keyword(s):  
Bayesian Approach ◽  
Regime Switching ◽  
Equity Markets ◽  
Equity Returns ◽  
Information Criteria ◽  
Model Choice ◽  
Markov Regime Switching ◽  
Volatility Models ◽  
Volatility Model ◽  
Sub Saharan

We adopt a granular approach to estimating the risk of equity returns in sub-Saharan African frontier equity markets under the assumption that, returns are influenced by developments in the underlying economy. Four countries were studied – Botswana, Ghana, Kenya and Nigeria. We found heterogeneity in the evolution of volatility across these markets and also that two-regime switching volatility models describe better the heteroscedastic returns generating processes in these markets using the deviance information criteria. We backtest the results to assess whether the models are a good fit for the data. We concluded that, the selected models are the most suitable for predicting the volatility of future returns in the markets studied. 

scholarly journals Modeling Precious Metal Returns through Fractional Jump-Diffusion Processes Combined with Markov Regime-Switching Stochastic Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 407
Author(s):  
Martha Carpinteyro ◽  
Francisco Venegas-Martínez ◽  
Alí Aali-Bujari
Keyword(s):  
Decision Making ◽  
Stochastic Volatility ◽  
Regime Switching ◽  
Precious Metal ◽  
Precious Metals ◽  
Stochastic Volatility Model ◽  
Decision Making Process ◽  
Markov Regime Switching ◽  
Volatility Model ◽  
Gold Silver

This paper is aimed at developing a stochastic volatility model that is useful to explain the dynamics of the returns of gold, silver, and platinum during the period 1994–2019. To this end, it is assumed that the precious metal returns are driven by fractional Brownian motions, combined with Poisson processes and modulated by continuous-time homogeneous Markov chains. The calibration is carried out by estimating the Jump Generalized Autoregressive Conditional Heteroscedasticity (Jump-GARCH) and Markov regime-switching models of each precious metal, as well as computing their Hurst exponents. The novelty in this research is the use of non-linear, non-normal, multi-factor, time-varying risk stochastic models, useful for an investors’ decision-making process when they intend to include precious metals in their portfolios as safe-haven assets. The main empirical results are as follows: (1) all metals stay in low volatility most of the time and have long memories, which means that past returns have an effect on current and future returns; (2) silver and platinum have the largest jump sizes; (3) silver’s negative jumps have the highest intensity; and (4) silver reacts more than gold and platinum, and it is also the most volatile, having the highest probability of intensive jumps. Gold is the least volatile, as its percentage of jumps is the lowest and the intensity of its jumps is lower than that of the other two metals. Finally, a set of recommendations is provided for the decision-making process of an average investor looking to buy and sell precious metals.

A Markov regime switching approach for hedging energy commodities

2008 ◽  
Vol 32 (9) ◽  
pp. 1970-1983 ◽  
Author(s):  
Amir H. Alizadeh ◽  
Nikos K. Nomikos ◽  
Panos K. Pouliasis
Keyword(s):  
Regime Switching ◽  
Markov Regime Switching

scholarly journals Pemodelan Deteksi Krisis Keuangan di Indonesia Berdasarkan Indikator M2/Cadangan Devisa

2019 ◽  
Vol 16 (2) ◽  
pp. 98-103
Author(s):  
Aisyah Zahrotul Hidayah ◽  
Sugiyanto Sugiyanto ◽  
Isnandar Slamet
Keyword(s):  
Financial Crisis ◽  
Regime Switching ◽  
Structural Changes ◽  
Detection System ◽  
International Reserves ◽  
Markov Regime Switching ◽  
Volatility Models ◽  
Macroeconomic Indicators ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Very High

The banking crisis reflects the liquidity crisis and bankruptcy of banks in the financial system. The financial crisis that occurred in mid-1997 resulted in a financial crisis that had a severe impact on the Indonesian economy. This made it aware of the importance of building a financial crisis early detection system to prepare for a crisis. The crisis occurs due to several macroeconomic indicators undergoing structural changes (regimes) and contain very high fluctuations. Combined volatility models and Markov regime switching are very suitable for explaining crises. The M2/international reserves indicator from 1990 to 2018 was used to build a crisis model. The results showed that the Markov regime switching autoregressive conditional heteroscedasticity model MRS-ARCH(2,1) could explain the crisis that occurred in mid-1997. Based on this model, in the future the crisis might occur if the M2/international reserves indicator decreased minimum of 13%

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